Inspired by the training technique introduced in [1]}, we are motivated to deliver the expected distribution of the extrinsic information from the decoder to the NN by assuming that the extrinsic information follows a consistent Gaussian distribution [2]}, where the mean and variance can be computed from the inverse \(J\) -function as introduced in [3]}.
Note that in [1]}, the DNN treats the extrinsic information from the decoder as separate input neurons to the NN, i.e., the channel information and the extrinsic information from the decoder are fed into the NN separately. However, this largely increases the number of neurons in the NN and overloads the training process of the whole NN. Therefore, we propose to combine the LLRs of the channel information and the extrinsic information before sending the LLRs to the DL-SPDA, such that the DL-SPDA uses the combined information for further training.
